Large data sets may exist in various sizes and with various levels of organization. With big data comprising data sets as large as ever, the volume of data collected incident to the increased popularity of online and electronic transactions continues to grow. Billions of rows and hundreds of thousands of columns worth of data may populate a single table, for example. An example of the use of big data is in identifying and categorizing the relative standing (e.g., relative size) of a business relative to other businesses in its industry, which is frequently a key priority for transaction account issuers. Transactions processed by the transaction account issuer are massive in volume and comprise tremendously large data sets.
Due to significant informational deficiencies, private business opacity, and relative variations in transaction volumes among different industries, data establishing both objective and relative transaction volume and relative business size are incomplete and inaccurate. This data gap confuses and frustrates the identification and categorization of transaction data and related businesses, and obscures the identity and categorization of real-world entities and individuals behind transactions, while also hampering data analytics.